Hi Robert
2008/7/17 Robert Kern <robert.kern@gmail.com>:
> In [42]: smallcube = cube[idx_i,idx_j,idx_k]
Fantastic -- a good way to warm up the brain-circuit in the morning!
Is there an easy-to-remember rule that predicts the output shape of
the operation above? I'm trying to imaging how the output would
change if I altered the dimensions of idx_i or idx_j, but it's hard.
It looks like you can do all sorts of interesting things by
manipulation the indices. For example, if I take
In [137]: x = np.arange(12).reshape((3,4))
I can produce either
In [138]: x[np.array([[0,1]]), np.array([[1, 2]])]
Out[138]: array([[1, 6]])
or
In [140]: x[np.array([[0],[1]]), np.array([[1], [2]])]
Out[140]:
array([[1],
[6]])
and even
In [141]: x[np.array([[0],[1]]), np.array([[1, 2]])]
Out[141]:
array([[1, 2],
[5, 6]])
or its transpose
In [143]: x[np.array([[0,1]]), np.array([[1], [2]])]
Out[143]:
array([[1, 5],
[2, 6]])
Is it possible to separate the indexing in order to understand it
better? My thinking was
cube_i = cube[idx_i,:,:].squeeze()
cube_j = cube_i[:,idx_j,:].squeeze()
cube_k = cube_j[:,:,idx_k].squeeze()
Not sure what would happen if the original array had single dimensions, though.
Back to the original problem:
In [127]: idx_i.shape
Out[127]: (10, 1, 1)
In [128]: idx_j.shape
Out[128]: (1, 15, 1)
In [129]: idx_k.shape
Out[129]: (10, 15, 7)
For the constant slice case, I guess idx_k also have been (1, 1, 7)?
The construction of the cube could probably be done using only
cube.flat = np.arange(nk)
Fernando is right: this is good food for thought and excellent
cookbook material!
Regards
Stéfan